The accurate annotation of protein localization is crucial in understanding protein function in tandem with a broad range of applications such as pathological analysis and drug design. Since most proteins do not have experimentally-determined localization information, the computational prediction of protein localization has been an active research area for more than two decades. In particular, recent machine-learning advancements have fueled the development of new methods in protein localization prediction. In this review paper, we first categorize the main features and algorithms used for protein localization prediction. Then, we summarize a list of protein localization prediction tools in terms of their coverage, characteristics, and accessibility to help users find suitable tools based on their needs. Next, we evaluate some of these tools on a benchmark dataset. Finally, we provide an outlook on the future exploration of protein localization methods.

蛋白质定位的准确注释对于理解蛋白质功能以及病理分析和药物设计等广泛应用至关重要。由于大多数蛋白质没有实验确定的定位信息，因此二十多年来，蛋白质定位的计算预测一直是一个活跃的研究领域。特别是，最近机器学习的进步推动了蛋白质定位预测新方法的发展。在这篇综述论文中，我们首先对用于蛋白质定位预测的主要特征和算法进行了分类。然后，我们从覆盖范围、特征和可访问性方面总结了一系列蛋白质定位预测工具，以帮助用户根据自己的需求找到合适的工具。下一个，我们在基准数据集上评估其中一些工具。最后，我们对蛋白质定位方法的未来探索进行了展望。